Path planning for AGVs based on a hybrid particle swarm optimization and ant colony optimization algorithm

To address the issues of low search efficiency and slow convergence in the later stages of traditional ant colony optimization (ACO) algorithms for AGV path planning, this paper proposes an AGV path planning method based on a hybrid particle swarm optimization (PSO) and ant colony optimization (ACO) algorithm. Firstly, a grid map method is used to establish the environmental model. Then, a diversity enhancement mechanism and dynamic adjustment of the inertia factor in the PSO are introduced to perform initial path planning. Based on the initially planned path, pheromone-rich regions are identified on the map. Improvements are made to the pheromone update mechanism and the heuristic function in the node transition probability formula of the ACO to enhance the algorithm's convergence. The improved ACO is then used for path planning. Finally, a secondary optimization is performed on the planned path to obtain the optimal path. The effectiveness of the hybrid algorithm is validated through a comparison with results from a solely improved ACO.